Precursor Systems Analysis for Automated Highway Systems: Volume II-Lateral and Longitudinal Control Final Report

Dickerson, J.; Lai, M.; Ioannou, P.; Chien, C.C.; Kanaris, A.; Damos, Diane L.; Smith, D; Shulman, M.; Eckert, S. · 1994 · ROSA P / United States. Department of Transportation. Federal Highway Administration

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Summary

This report, produced by the Federal Highway Administration as part of the Automated Highway System (AHS) Precursor Systems Analyses, addresses the technical requirements, risks, and evolutionary pathways for automating vehicle lateral and longitudinal control. The study aims to identify high-level issues associated with transitioning from manual to automated driving, specifically focusing on performance, reliability, and human factors. The analysis defines five Evolutionary Representative System Configurations (ERSCs) that characterize the gradual introduction of automation, ranging from basic speed and headway maintenance to fully autonomous lateral and longitudinal control. The methodology involves a detailed analysis of each ERSC, evaluating sensor, actuator, and controller requirements necessary to meet specific performance and reliability standards. Performance requirements focus on driver comfort, acceptance, and safe transitions between automatic and manual control. Reliability requirements are quantified using National Highway Traffic Safety Administration accident rate data to determine the necessary redundancy and structural complexity for automatic systems. The study also examines headway selection and highway capacity, modeling stopping scenarios and collision-free vehicle following to determine minimum safe distances based on vehicle deceleration capabilities, reaction times, and road-tire friction coefficients. Additionally, the report analyzes the impact of roadway traffic controllers on traffic flow stability, using simulations to compare disturbance propagation with and without automated roadway management. Key findings indicate that automated systems can significantly improve traffic flow by reducing travel time and avoiding congestion, particularly when roadway controllers dampen disturbances caused by incidents. The analysis derives specific reliability functional requirements for each ERSC, showing that as automation increases, the required system redundancy and complexity rise to maintain safety levels comparable to or better than human drivers. For instance, ERSC 1 introduces speed and headway maintenance with rear-end collision warning, while later configurations add automatic lane keeping and changing. The study highlights that sensor range and detection accuracy are critical for maintaining safe headways, especially under varying friction conditions. Furthermore, the reliability analysis demonstrates that automatic systems must account for driver degradation and failure modes, necessitating robust design frameworks to ensure safety during both normal operation and fallback to manual control. The significance of this work lies in providing a structured framework for the development and deployment of automated highway systems. By defining clear evolutionary steps and quantifying the associated technical and reliability requirements, the report offers a basis for estimating the cost and difficulty of implementing AHS. It underscores the importance of integrating vehicle automation with roadway infrastructure to maximize capacity benefits and safety. The findings support the feasibility of automated control systems while highlighting the critical need for rigorous reliability engineering and human factors consideration to ensure successful integration into the national transportation network.

Key finding

The report defines five evolutionary representative system configurations for automated highway lateral and longitudinal control, deriving specific performance and reliability requirements for each stage to enable safe transitions between manual and automatic driving.

Methodology

theoretical

Provenance

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Topics

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• situational awareness
• automation surprise
• lane positioning
• teleoperation remote driving
• automation
• adaptive driving beam

Information type

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• Theoretical Contribution: computational model